# Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing

“Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing” (2021), by Vishal Monga et al. Engineering research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Vishal Monga, Yuelong Li, Yonina C. Eldar
- Published: February 25, 2021
- Field: Engineering
- DOI: https://doi.org/10.1109/msp.2020.3016905
- Free PDF: https://arxiv.org/pdf/1912.10557

## Abstract

Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. An emerging technique called algorithm unrolling, or unfolding, offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are widely used in signal processing and deep neural networks. Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. More recently, this direction has attracted enormous attention, and it is rapidly growing in both theoretic investigations and practical applications. The increasing popularity of unrolled deep networks is due, in part, to their potential in developing efficient, high-performance (yet interpretable) network architectures from reasonably sized training sets.

Canonical page: https://nerdi.pages.dev/paper/W2995038946
Read three real papers a day in Nerdi (iPhone): https://apps.apple.com/app/apple-store/id6813826387?pt=128757794&ct=web-paper&mt=8